With the rapid advancement of technology, citizens of modern cities are getting access to smart services such as mobile internet, GPS, telecommunication and transportation network that enhances citizenship experience, such cities are called smart cities. Public alerting system is one such important smart city service. A public alert system conveys alerts regarding various events such as traffic disruption, crime, hazard and emergency situations in a city to its citizens. Public Alert systems have been in existence for quite long. Earlier the alerts were conveyed through radio and TV, but now with the emergence of Internet and. Telecommunication email alerts, SMS and real-time posts on social platform have become the preferred medium of communication in the alert systems. But, to ensure that any city based incident is never missed, the alert system needs to sense every change in environmental conditions in the city. This is made possible as modern cities are gradually getting equipped with various types of sensors that can sense events as and when it happens.
Physical sensors like thermometer and hygrometer give readings based on which a concerned authority can raise weather alerts of high humidity and heat. The concerned authority can also raise alerts about traffic disruptions and criminal activities by analyzing live streaming from video surveillance equipment fitted at various locations in a city. Another interesting way to sense events is crowd sensing. In crowd sensing, the data is posted by citizens on social platform based on their personal observation or experience, this data form the basis of alerts. There are also other channels based on which alerts are forwarded by the concerned authority to the citizens. In some cities only emergency alerts like flood or high tide that affect a large population are forwarded. Some other modern cities in the world have facility to provide more specific subscription based alerts and information services to its citizens.
As the number of events occurring in the city is large, a citizen may be flooded with alerts that are irrelevant. Some city alert services provide a choice to the subscriber/user to state a set of location preferences on a web portal. However as the citizen's location in a city is dynamic, prior selection of preferred location leads to delivery of irrelevant alerts and some important alerts are missed out. For instance, if a subscriber (who is a citizen of New York) has stated that he stays in Columbus Avenue, but currently he is at Wall Street which is mid-way to office, so an alert about a traffic jam at Columbus Avenue is irrelevant to him at that moment. Similarly if a traffic incident happens in Fulton Street which is his next route segment on way, he will not be alerted as Fulton Street as a location of interest to the user is not stated in his preference.
Apart from events that will affect the citizen directly, a citizen may be interested in events that are affecting or will affect his/her colleagues. Hence, location based preferences are alone not good enough to filter alerts. As there are many events happening in a city frequently, it is difficult to notify the citizens about the relevant events that are currently affecting them or will affect them in future. Such a selective notification will enhance the citizen's ubiquitous experience.
One way to achieve such intelligent alerting system is to use the citizen's preferences, social web presence and demographic information to ensure that a citizen receives personalized alerts. Another way is to make the system context-aware such that the system will sense the current context of the user and send relevant alerts. Contextual information such as location of the citizen, time of day, weather conditions, and companion is considered in this approach. Another approach by Montanari et al that was presented as a research paper titled “Architecture for an automatic customized warning system. (2007), pp. 32-39” at IEEE conference on Intelligence and Security Informatics discloses architecture for a policy driven public warning system, wherein the alerts are disseminated based on some pre-set policies. The alert topics are represented as triples while the crisis policies are maintained as rules which are fired when corresponding conditions are met (like if an alert is severe, activate all mediums to convey the alert such as radio, TV and city alarms). Further, Hu et al in their research paper titled “Contextualized information assessment in smart cities” presented in 1st Workshop on Intelligent Agents in Urban Simulation and Smart Cities (2012), pp. 11-16 proposes a rule-based architecture for provision of information relevant to user's activities in a city. The architecture also introduces an ontology-based context model to characterize the possible situations of a citizen in a city. The concept of personalization and context-awareness can be further enhanced if the information gathered from these approaches can be extended. The extension of freshly gathered information into new information using existing knowledge is called as reasoning. Reasoning on streams, or stream reasoning, is an important mechanism to be used in such smart alerting systems. Note that information available from various sources is in general unstructured information. This unstructured information has to be converted into a structured format so as to facilitate reasoning.
Real-time streams of event and context need to be reasoned upon to derive richer knowledge. A stream reasoning system does reasoning on the fly on streams of knowledge combined with background knowledge to produce meaningful entailments. However the lifetime of the entailments is short-lived as the facts causing the entailment generation are themselves short-lived due to the dynamic nature of the real-time streams of knowledge. Adding to it, the stream reasoner works on a standard data structure such as triple or N-triple format. Considering the amount of information generated through social network, sensor network and changing contextual behavior of users, it is a time consuming process to convert this information into triple and N-triple format. Even if all the knowledge is converted into a standard structure as per the standard requirements of the stream reasoner, the stream reasoner requires a lot of time for loading and unloading this information to find out the relevant alerts associated with a particular citizen. Here the challenge with the stream reasoner is to process the structured knowledge at a faster speed and to enhance the overall performance of the system by reducing memory overhead, achieving scalability and generating efficient results.
Hence, there is a need in the art to address the problem discussed earlier by designing a near real-time system which can deliver personalized alerts based on citizen's preferences, demographics and information captured from the social web. There is a need to derive contextual information associated with the citizen and generate alerts depending on the citizen's current and future context. There is a need to reason the background knowledge, dynamically changing knowledge on the fly that aids the alerting system to decide whether to forward an alert to a particular citizen or not. There is a need to bring together context-awareness, personalization, near-real-time and knowledge-based aspects to the public alert domain to achieve the goal of providing a superior service to the citizens of smart cities. Further there is a need to filter the information collected from all these sources by means of prior-reasoning before applying stream reasoning to reduce the processing time required by the stream reasoner to derive the set of affected citizens.